Processing of images is increasingly gaining use in applications such as video editing, video compression, security and surveillance, human computer interaction and the like. The processing of images typically involves detecting a face in a sequence of image frames and/or video frames, and, then subsequently tracking the face in subsequent images.
Face detection is commonly performed using a pattern recognition based detector which is a computationally intensive task in order to be used for every frame. On detecting a face, tracking of the face is performed. The tracking of the face requires that the face is not missed in any of the following frames, implying that the tracking of the face must be robust.
However, the tracking of the face may not be robust, as the performance may be limited by an accuracy of the pattern recognition based detector. This may lead to missing some faces in-between frames. On losing track of the face, a detection of the face may be performed again for detecting the face and subsequently tracking the face. As a result, the computationally intensive pattern recognition based detector may need to be utilized again, thereby resulting in increased number of computations per frame.